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Detection of Copy Number Alterations Using Single Cell Sequencing
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A zero-agnostic model for copy number evolution in cancer.

Henri Schmidt1, Palash Sashittal1, Benjamin J Raphael1

  • 1Department of Computer Science, Princeton University, Princeton, New Jersey, United States of America.

Plos Computational Biology
|November 9, 2023
PubMed
Summary

We developed Lazac, an efficient algorithm for inferring tumor evolutionary history using copy number profiles. Lazac improves upon existing methods for copy number phylogenies, aiding cancer research.

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Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • Single-cell DNA sequencing generates copy number profiles for thousands of cells.
  • Inferring tumor evolution from copy number aberrations requires specialized phylogenetic models.
  • The copy number transformation (CNT) model represents genomic changes but lacks efficient phylogeny inference algorithms.

Purpose of the Study:

  • Introduce a simplified model, zero-agnostic copy number transformation (ZCNT), for copy number phylogenies.
  • Develop efficient algorithms for inferring tumor evolutionary history under the ZCNT model.
  • Provide a practical algorithm, Lazac, for solving the large parsimony problem in copy number profiles.

Main Methods:

  • Derived a closed-form expression for the ZCNT distance, proving it forms a metric.
  • Developed polynomial-time algorithms for relaxations of the small parsimony problem using ZCNT distance.
  • Extended ZCNT algorithms to create Lazac for the large parsimony problem.

Main Results:

  • The ZCNT distance closely approximates the biologically realistic CNT distance.
  • Lazac demonstrates superior performance over existing methods for copy number phylogeny inference on simulated and real data.
  • Efficient algorithms were derived for small and large parsimony problems on copy number profiles.

Conclusions:

  • The ZCNT model and its associated algorithms offer a computationally efficient approach to inferring tumor phylogenies.
  • Lazac provides a significant advancement in analyzing tumor evolution from single-cell copy number data.
  • This work facilitates more accurate reconstruction of cancer evolutionary histories.